
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "88"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459998	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.068347	0.614976	0.573856	1.051646	0.400820	-0.640514	-0.515388	-0.204411	0.6240	0.6410	0.3618	nan	nan
2459997	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.034603	0.534732	0.520387	1.234486	-0.300319	-0.822789	-0.185076	-0.166633	0.6326	0.6545	0.3650	nan	nan
2459996	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.418641	1.405463	1.050635	1.874652	-0.159973	-0.614029	1.123712	0.510558	0.6380	0.6540	0.3769	nan	nan
2459995	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.616249	0.640743	0.590307	1.347318	-0.274257	0.092252	1.903899	0.520012	0.6262	0.6430	0.3709	nan	nan
2459994	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.709992	-0.016304	0.545089	1.152392	-0.053457	-0.572365	2.195089	0.451284	0.6189	0.6343	0.3692	nan	nan
2459993	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.856590	-0.305623	0.287220	0.939691	-0.361786	-0.310416	2.369893	0.338884	0.6118	0.6387	0.3786	nan	nan
2459991	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.963520	0.072520	0.499654	0.976591	-0.156068	-0.598760	0.982521	0.121564	0.6379	0.6482	0.3664	nan	nan
2459990	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.754019	-0.261862	0.552173	0.827547	0.070421	-0.709833	0.811485	-0.267222	0.6333	0.6459	0.3639	nan	nan
2459989	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.884130	-0.128300	0.473783	0.956855	-0.229543	-1.154602	-0.216158	-0.558171	0.6272	0.6443	0.3676	nan	nan
2459988	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	1.046706	-0.105540	0.452720	0.734902	-0.315315	-1.037117	1.549457	0.140153	0.6272	0.6427	0.3598	nan	nan
2459987	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.243761	0.231930	0.405142	0.990637	-0.366466	-0.819042	-0.120415	-0.424606	0.6383	0.6536	0.3592	nan	nan
2459986	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.162136	0.342056	0.460501	0.912738	-0.737745	-0.594656	2.561241	0.600948	0.6564	0.6762	0.3153	nan	nan
2459985	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	0.549133	0.512508	0.406291	1.015679	-0.712434	-0.937028	6.767400	2.249529	0.6355	0.6491	0.3653	nan	nan
2459984	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.828785	0.671079	0.373718	1.122473	-0.600356	-0.563662	3.383055	0.878111	0.6541	0.6654	0.3439	nan	nan
2459983	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.414494	0.740137	0.495731	0.848255	-0.499440	-0.718718	1.743077	-0.059610	0.6658	0.6904	0.3039	nan	nan
2459982	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.249787	0.196157	0.665556	0.895459	-0.112378	-0.637698	0.232995	0.362942	0.7251	0.7250	0.2595	nan	nan
2459981	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.605058	0.020899	0.472270	0.719640	-0.462857	-0.771746	1.829718	0.224719	0.6382	0.6524	0.3596	nan	nan
2459980	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.002214	-0.002214	0.308481	0.790049	-0.521891	-0.667896	0.876288	0.577105	0.6855	0.6980	0.2840	nan	nan
2459979	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.211595	-0.213986	0.221833	0.627369	-0.268725	-0.983642	1.008276	0.208365	0.6309	0.6481	0.3610	nan	nan
2459978	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.628786	-0.121098	0.271260	0.661137	-0.300594	-0.834865	0.617558	-0.116435	0.6320	0.6478	0.3665	nan	nan
2459977	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.743980	0.335739	0.279174	0.782715	-0.364763	-0.500029	2.020132	0.626130	0.5986	0.6167	0.3338	nan	nan
2459976	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.635152	0.547959	0.346504	0.756359	-0.195289	-0.009130	0.095072	-0.356776	0.6383	0.6543	0.3574	nan	nan

Antenna Season Report Notebook¶

Antenna 88 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 88, Node N09:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 88: 2459998

Antenna 88: 2459997

Antenna 88: 2459996

Antenna 88: 2459995

Antenna 88: 2459994

Antenna 88: 2459993

Antenna 88: 2459991

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Antenna 88: 2459989

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Antenna 88: 2459987

Antenna 88: 2459986

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Antenna 88: 2459980

Antenna 88: 2459979

Antenna 88: 2459978

Antenna 88: 2459977

Antenna 88: 2459976

Antenna Season Report Notebook¶

Antenna 88 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 88, Node N09:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 88: 2459998

Antenna 88: 2459997

Antenna 88: 2459996

Antenna 88: 2459995

Antenna 88: 2459994

Antenna 88: 2459993

Antenna 88: 2459991

Antenna 88: 2459990

Antenna 88: 2459989

Antenna 88: 2459988

Antenna 88: 2459987

Antenna 88: 2459986

Antenna 88: 2459985

Antenna 88: 2459984

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Antenna 88: 2459982

Antenna 88: 2459981

Antenna 88: 2459980

Antenna 88: 2459979

Antenna 88: 2459978

Antenna 88: 2459977

Antenna 88: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶